Adaptive Processing of Technological Time Series for Forecasting Based on Neuro-Fuzzy Networks
Keywords:
technological time series, neural network, neuro-fuzzy network, genetic algorithms, hybrid model
Abstract
Methodological bases for identification, data processing for forecasting technological time series based on the synthesis of soft computing apparatus (dynamic models, neural networks, neuro-fuzzy networks, genetic algorithms) in various combinations have been developed. A generalized prediction optimization algorithm based on a hybrid model with mechanisms for determining and adjusting the weights of neurons, coefficients of synaptic connections, activation functions, determining the number of layers and neurons in the layers of neural networks with a rational architecture is proposed.
References
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3. Wong, W. K., Xia, M., & Chu, W. C. (2010). Adaptive neural network model for time-series forecasting. European Journal of Operational Research, 207(2), 807-816.
4. Mosallam, A., Medjaher, K., & Zerhouni, N. (2016). Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, 27(5), 1037-1048.
5. Tra, V., Kim, J., Khan, S. A., & Kim, J. M. (2017). Bearing fault diagnosis under variable speed using convolutional neural networks and the stochastic diagonal levenberg-marquardt algorithm. Sensors, 17(12), 2834.
6. Ярушкина Н.Г. Основи нечетких и гибридних систем: Учебное пособие. – М.:Финанси и статистика. 2004. 320 c.
7. Ярушкина, Н. Г. Интеллектуалний анализ временних рядов: учебное пособие / Н.Г. Ярушкина, Т.В. Афанасева, И.Г. Перфилева. – Уляновск: УлГТУ, 2010. – 320 п.
8. Тимшина, Д. В. (2014). Нечёткая логика и анализ эффективности инвестиционных проектов в среде matlab, fuzzy logic toolbox. Вестник Академии знаний, (1 (8)).
9. Jumanov, I. I., & Karshiev, K. B. (2019). Analysis of efficiency of software tools optimizing the information reliability of electronic documents in automated control systems. Chemical Technology, Control and Management, 2019(2), 57-66.
10. Ibragimovich, J. I., Isroilovich, D. O., & Maxmudovich, X. S. (2020, November). Effective recognition of pollen grains based on parametric adaptation of the image identification model. In 2020 International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1-5). IEEE. DOI: 10.1109/ICISCT50599.2020.9351486
11. Холмонов, С. М. (2017). Оптимизация обработки данных на основе модифицированного обучения нейронной сети и сегментации случайных временных процессов. Проблемы информатики, (1 (34)), 52-61.
12. Djumanov, O. I., Kholmonov, S. M., & Shukurov, L. E. (2021). Optimization of the credibility of information processing based on hyper semantic document search. Theoretical & Applied Science, (4), 161-164.
13. Jumanov, I., Djumanov, O., & Safarov, R. (2021). Improving the quality of identification and filtering of micro-object images based on neural networks. In E3S Web of Conferences (Vol. 304, p. 01007). EDP Sciences.
14. Jumanov, I. I., & Kholmonov, S. M. (2020). Optimization of identification under the conditions of low reliability of information and parametric uncertainty of non-stationary objects. Chemical Technology, Control and Management, 2020(5), 104-112.
15. Israilovich, D. O., Makhmudovich, K. S., & Uktomovich, Y. F. (2021). Increasing The Credibility Of Forecasting Random Time Series Based On Fuzzy Inference Algorithms. International Journal of Progressive Sciences and Technologies, 26(1), 12-15.
Published
2022-02-16
How to Cite
Ibragimovich, J. I., & Baxromovna, M. M. (2022). Adaptive Processing of Technological Time Series for Forecasting Based on Neuro-Fuzzy Networks. International Journal of Human Computing Studies, 4(2), 30-35. https://doi.org/10.31149/ijhcs.v4i2.2721
Section
Articles
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